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Table 6:Statistical summary for orthogonality Orthogonality
"... In PAGE 9: ...1.3 Orthogonality Based on Table6 , which data are again not yet disaggregated with respect to expertise, orthogonality is 97%, while variance is 2. This expresses that, in the average, programmers commonly percept ODC with respect, and tend to provide just one classification per defect, whatever is their expertise.... ..."
Table 6 shows statistical summary for Orthogonality, and Figure 8 presents the evolution of orthogonality in time.
"... In PAGE 7: ...Table 5: Orthogonality versus programming language Language Orthogonality Java C++ Average (%) 96 97 Variance 6 7 Table6 :Statistical summary for orthogonality Orthogonality Average (%) 97 Variance 2 0 4 8 12 16 20 24 3500 4500 5500 6500 7500 8500 9500 10500 11500 Time (sec.) O r t h o g o n a l i t y Figure 8: Orthogonality in time 4.... In PAGE 9: ...1.3 Orthogonality Based on Table6 , which data are again not yet disaggregated with respect to expertise, orthogonality is 97%, while variance is 2. This expresses that, in the average, programmers commonly percept ODC with respect, and tend to provide just one classification per defect, whatever is their expertise.... ..."
Table 1: 13 Temporal Interval Relations The temporal interval algebra is essentially topological relations in one dimensional space en- hanced by the distinction of the order of the space. A 2D space is usually represented by two orthogonal axes, x and y. An object approximated by an MBR can be represented by two points (such as lower left corner and top right corner). These two points can be projected onto the x and y axes and each projection can be seen as an interval. It is obvious that the MBRs approximation is the ideal technique to capture topological relations if the interval algebra is used. Using the interval algebra to capture both directional and topological relations of spatial objects can o er more information about spatial relations between objects as compared to traditional methods [NSN95]. In other words, it has greater expressive power than traditional methods.
1996
"... In PAGE 10: ... 3.1 The Temporal Interval Algebra Allen [All83] gives a temporal interval algebra ( Table1 ) for representing and reasoning about temporal relations between events represented as intervals. These temporal relations have often been cited [Bee89, SF95, NSN95] for their simplicity and ease of implementation with constraint... ..."
Cited by 15
Table 1: Iterative MMSE algorithm with orthogonalization (IMMSE-O)
in Spectrally efficient communication for wireless sensor networks using a cooperative MIMO technique
"... In PAGE 7: ...Following the steps in Table1 , the collector then computes the required uplink power Pi and rate Ri via water-pouring. It then transmits this information to the sensors.... In PAGE 7: ... To obtain the remaining eigenvectors, an orthogonalization procedure is necessary. The Iter- ative MMSE with Orthogonalization (IMMSE-O) algorithm is described in Table1 . It should be emphasized that the orthogonalization is performed at the collector and the sensors can sequen- tially estimate the elements of the eigenvectors in a distributed manner.... ..."
Cited by 1
Table 1: Comparing the expressive power of various matching semantics.
2005
"... In PAGE 11: ...uzzy operators, as we discussed above. Third, the values that are involved in the expressions (e.g., Far and Cheap) can be fuzzy values. Table1 provides a concise albeit informal comparison of the expressive power provided by conventional tuples, BooleanTuples, and FuzzyTuples. 4.... ..."
Table 2: Expressive power of RDF/S query language
2002
"... In PAGE 16: ...Table2 hosts a comparison of RDF/S query languages according to five axes: Modeling constructs supported, Ontology Querying, Data Querying, Data/Ontology Querying and Additional Features provided. Regarding the first comparison axis (Modeling constructs), we can observe from Table 2 that all query languages taking part in the comparison, i.... In PAGE 16: ...supported, Ontology Querying, Data Querying, Data/Ontology Querying and Additional Features provided. Regarding the first comparison axis (Modeling constructs), we can observe from Table2 that all query languages taking part in the comparison, i.e.... In PAGE 16: ... Furthermore, the ability of query languages to perform Data querying is of major importance. Based on this set of criteria, we can note from Table2 that all query language provide constructs for finding the extent of a class or property, either directly or transitively. What most query languages do not support is set-based operations (union, intersection, difference) as well as arithmetic operations on data values.... In PAGE 16: ...ime. The basic criterion for judging this ability is the use of generalized path expressions. Generalized path expressions are very useful primitives because they allow data and ontology to be uniformly queried. As indicated from Table2 , only RQL is capable of incorporating knowledge from ontologies into data querying. In fact, RQL features generalized path expressions with variables on labels of both nodes and edges.... In PAGE 16: ... To evaluate the effectiveness of the query languages when used in large-scale Semantic Web applications we can also use a set of criteria referring to Additional Features supported by the query languages. Table2 records whether the query languages under evaluation support aggregate, grouping and sorting functions. More specifically, RDFQL supports only a count function, VERSA supports min and max, while RQL features min, max, count, average and sum functions, as known from relational databases.... ..."
Cited by 20
Table 2. Power consumption expressed in mW / MHz.
2002
Cited by 1
Table 3: Expressive power characterized in terms of relational query languages
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